The final grade is based on performance, a written report on each day of the practical course, and one or two short oral presentations.

The focus is on the practical analysis of so-called next generation sequencing
data. Students learn the use of tools for evaluating this data. This practical
course uses real-life data; the focus is on the entire process of evaluating experimental
data, from quality analyses to in-depth statistical analyses; various
methods are compared. Topics include de-novo assembly, expression count calculation,
normalization and clustering, machine learning methods and their
application to expression data, statistical methods for calculating differential
expressions, visualization methods, and enrichment methods.

Students will gain practical experience in designing and programming bioinformatics
software for analyzing NGS data. They will be able to use libraries
and frameworks, and will acquire knowledge or extend their knowledge of Java
or C++ and R. By working together in groups, students obtain teamwork and
collaboration skills, and they will learn about project organization and presentation
techniques. Students will know about the strengths and weaknesses
and about the limitations of various methods for evaluating high-throughput
transcriptomic data, and will be able to describe and evaluate these methods.

Will be provided at the beginning of the course, if necessary.